13. What are the steps to be followed to use the gradient descent algorithm? There are five main steps that are used to initialize and use the gradient descent algorithm:Initialize biases and weights for the network Send input data through the network (the input layer) Calculate the difference...
Gradient descent is an optimization algorithm used to minimize the loss function in machine learning by iteratively adjusting model parameters in the direction of steepest descent. 14. What is deep learning? Deep learning is a subfield of machine learning that focuses on neural networks with multiple...
使用一个样本为例简单说明,此时二次代价函数为: $$ J = \frac{(y-a)^2}{2} $$ 假如使用梯度下降法(Gradient descent)来调整权值参数的大小,权值$w$和偏置$b$的梯度推导如下: $$ \frac{\partial J}{\partial b}=(a-y)\sigma'(z) $$ 其中,$z$表示神经元的输入...
A decision tree is a supervised learning algorithm that is used for both classification and regression. Hence, in this case, the dependent variable can be both a numerical value and a categorical value. Here, each node denotes the test on an attribute, and each edge denotes the outcome of ...
K-means clustering is an unsupervised machine learning algorithm that divides data points into number of groups (K) depending on their features. It works by iteratively assigning each data point to the nearest cluster centroid and then updating the centroids to reflect the average of all points ...
Gradient descent is an optimization algorithm used to find the values of parameters (coefficients) of a function (f) that minimizes a cost function (cost). Gradient descent is best used when the parameters cannot be calculated analytically (e.g. using linear algebra) and must be searched for ...
Option C is the description of gradient descent. Q6.What are the steps for using a gradient descent algorithm? Calculate error between the actual value and the predicted value Reiterate until you find the best weights of network Pass an input through the network and get values from output layer...
18. Discuss ‘Naive’ in a Naive Bayes algorithm? It is a classification technique based on Bayes’ Theorem with an assumption of independence among predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence ...
It depends a lot on the data one is dealing with and the initial values of the learning parameter. 27. What is the difference between Supervised Learning and Unsupervised Learning? Supervised Learning Unsupervised Learning If an algorithm learns something from the training data so that the ...
Adaptive Moment Estimation or Adam optimization is an extension to the stochastic gradient descent. This algorithm is useful when working with complex problems involving vast amounts of data or parameters. It needs less memory and is efficient. ...